Specific computational tools assist geologists in identifying and classifying the lithology of rocks in oil well exploration,reduc-ing costs,and enhancing operational efficiency.Machine learning methods integrate a vast amount of information,enabling efficient pat-tern recognition and accurate decision-making.This article categorizes the lithology of five oil wells in the Norwegian Sea,randomly di-viding the data into a training set(70%)and a test set(30%).Using multivariate well log parameter data for training and validation,the application effectiveness of models such as Multilayer Perceptron(MLP),Decision Tree,Random Forest,and XGBoost is com-pared.The research results indicate that the XGBoost model outperforms others in terms of data generalization,achieving an accuracy of 95%.The Random Forest model follows with an accuracy of 94%.Meanwhile,Multilayer Perceptron(MLP)and Decision Tree models exhibit good robustness,with accuracies of 92%and 90%,respectively.
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